DeepMapScaler: a workflow of deep neural networks for the generation of generalised maps

References

• Aslan, S., Bildirici, I., Simav, O., & Cetinkaya, B. (2012). An incremental displacement approach applied to building objects in topographic mapping. In 13th ICA workshop on generalisation and multiple representation.
   Google Scholar
• Barrault, M., Regnauld, N., Duchêne, C., Haire, K., Baeijs, C., Demazeau, Y., Hardy, P., Mackaness, W., Ruas, A., & Weibel, R. (2001). Integrating multi-agent, object-oriented, and algorithmic techniques for improved automated map generalisation. In 20th international cartographic conference (Vol. 3, pp. 2110–2116). ICA.
   Google Scholar
• Beard, M. K. (1991). Constraints on rule formation. In B. P. Buttenfield & R. B. McMaster (Eds.), Map generalization: Making rules for knowledge representation (pp. 121–135). Longman.
   Google Scholar
• Benz, S. A., & Weibel, R. (2014). Road network selection for medium scales using an extended stroke-mesh combination algorithm. Cartography and Geographic Information Science, 41(4), 323–339. https://doi.org/10.1080/15230406.2014.928482
   Web of Science ®Google Scholar
• Brassel, K. E., & Weibel, R. (1988). A review and conceptual framework of automated map generalization. International Journal of Geographical Information Systems, 2(3), 229–244. https://doi.org/10.1080/02693798808927898
   Google Scholar
• Burghardt, D., & Cecconi, A. (2007). Mesh simplification for building typification. International Journal of Geographical Information Science, 21(3), 283–298. https://doi.org/10.1080/13658810600912323
   Web of Science ®Google Scholar
• Chen, X., Chen, S., Xu, T., Yin, B., Peng, J., Mei, X., & Li, H. (2020). SMAPGAN: Generative adversarial network-based semisupervised styled map tile generation method. IEEE Transactions on Geoscience and Remote Sensing, 59(5), 4388–4406. https://doi.org/10.1109/TGRS.2020.3021819
   Web of Science ®Google Scholar
• Chen, W., Wu, A., & Biljecki, F. (2021). Classification of urban morphology with deep learning: Application on urban vitality. Computers, Environment and Urban Systems, 90, 13. https://doi.org/10.1016/j.compenvurbsys.2021.101706
   Web of Science ®Google Scholar
• Courtial, A., El Ayedi, A., Touya, G., & Zhang, X. (2020). Exploring the potential of deep learning segmentation for mountain roads generalisation. ISPRS International Journal of Geo-Information, 9(5), 338. https://doi.org/10.3390/ijgi9050338
   Web of Science ®Google Scholar
• Courtial, A., Touya, G., & Zhang, X. (2021a). Can graph convolution networks learn spatial relations? Abstracts of the ICA, 3, 1–2. https://doi.org/10.5194/ica-abs-3-60-2021
   Google Scholar
• Courtial, A., Touya, G., & Zhang, X. (2021b). Generative adversarial networks to generalise urban areas in topographic maps. In The international archives of the photogrammetry, remote sensing and spatial information sciences (Vol. XLIII-B4-2021, pp. 15–22). Copernicus GmbH. ( ISSN: 1682-1750).
   Google Scholar
• Courtial, A., Touya, G., & Zhang, X. (2022a). Constraint-based evaluation of map images generalized by deep learning. Journal of Geovisualization and Spatial Analysis, 6(1). https://doi.org/10.1007/s41651-022-00104-2
   Google Scholar
• Courtial, A., Touya, G., & Zhang, X. (2022b). Deriving map images of gen- eralised mountain roads with generative adversarial networks. International Journal of Geographical Information Science, 37(3), 499–528. https://doi.org/10.1080/13658816.2022.2123488
   Web of Science ®Google Scholar
• Courtial, A., Touya, G., & Zhang, X. (2022c). Representing vector geographic information as a tensor for deep learning based map generalisation. In E. Parseliunas, A. Mansourian, P. Partsinevelos, & J. Suziedelyte-Visockiene (Eds.), AGILE 2022 (Vol. 3, pp. 32). Copernicus Publications. https://hal.archives-ouvertes.fr/hal-03695681
   Google Scholar
• Duchêne, C., Touya, G., Taillandier, P., Gaffuri, J., Ruas, A., & Renard, J. (2018). Multi- agents Systems for cartographic generalization: Feedback from past and on-going research. Technical Report.
   Google Scholar
• Du, J., Wu, F., Xing, R., Gong, X., & Yu, L. (2021). Segmentation and sampling method for complex polyline generalization based on a generative adversarial network. Geocarto International, 37(14), 4158–4180. https://doi.org/10.1080/10106049.2021.1878288
   Web of Science ®Google Scholar
• Du, J., Wu, F., Yin, J., Liu, C., & Gong, X. (2022). Polyline simplification based on the artificial neural network with constraints of generalization knowledge. Cartography and Geographic Information Science, 49(4), 313–337. https://doi.org/10.1080/15230406.2021.2013944
   Web of Science ®Google Scholar
• Feng, Y., Thiemann, F., & Sester, M. (2019). Learning cartographic building generalization with deep convolutional neural networks. ISPRS International Journal of Geo-Information, 8(6), 258. https://doi.org/10.3390/ijgi8060258
   Web of Science ®Google Scholar
• Fu, H., Gong, M., Wang, C., Batmanghelich, K., Zhang, K., & Tao, D. (2019). Geometry- consistent generative adversarial networks for one-sided unsupervised domain mapping. In 2019 IEEE/CVF conference on computer vision and pattern recognition (CVPR) (pp. 2422–2431). IEEE.
   Google Scholar
• Garcia-Balboa, J. L., & Ariza-López, F. J. (2008). Generalization-oriented road line classification by means of an artificial neural network. Geoinformatica, 12(3), 289–312. https://doi.org/10.1007/s10707-007-0026-z
   Web of Science ®Google Scholar
• Hazırbaş, C., Ma, L., Domokos, C., & Cremers, D. (2016). FuseNet: Incorporating depth into semantic segmentation via fusion-based CNN architecture.
   Google Scholar
• Hu, Y., Liu, C., Li, Z., Xu, J., Han, Z., & Guo, J. (2022). Few-shot building footprint shape classification with relation network. ISPRS International Journal of Geo-Information, 11(5), 311. https://doi.org/10.3390/ijgi11050311
   Web of Science ®Google Scholar
• Iddianozie, C., & Mcardle, G. (2021). Transferable graph neural networks for inferring road type attributes in street networks. In IEEE access (Vol. 9, pp. 158331–158339). IEEE.
   Google Scholar
• Isola, P., Zhu, J.-Y., Zhou, T., & Efros, A. A. (2017). Image-to-image translation with conditional adversarial networks. In 2017 IEEE conference on computer vision and pattern recognition (CVPR) (pp. 5967–5976). ( ISSN: 1063-6919). IEEE.
   Google Scholar
• Janowicz, K., Gao, S., McKenzie, G., Hu, Y., & Bhaduri, B. (2020). GeoAI: Spatially explicit artificial intelligence techniques for geographic knowledge discovery and beyond. International Journal of Geographical Information Science, 34(4), 625–636. https://www.tandfonline.com/doi/full/10.1080/13658816.2019.1684500
   Web of Science ®Google Scholar
• Jiang, B., & Claramunt, C. (2004). A structural approach to the model generalization of an urban street network. GeoInformatica, 8(2), 157–171. https://doi.org/10.1023/B:GEIN.0000017746.44824.70
   Web of Science ®Google Scholar
• Kang, Y., Gao, S., & Roth, R. E. (2019). Transferring multiscale map styles using generative adversarial networks. International Journal of Cartography, 5(2–3), 115–141. https://doi.org/10.1080/23729333.2019.1615729
   Web of Science ®Google Scholar
• Kang, Y., Rao, J., Wang, W., Peng, B., Gao, S., & Zhang, F. (2020). Towards cartographic knowledge encoding with deep learning. In Autocarto (p. 6). cartogis.org.
   Google Scholar
• Kuhn, W. (2012, December). Core concepts of spatial information for transdisciplinary research. International Journal of Geographical Information Science, 26(12), 2267–2276. https://doi.org/10.1080/13658816.2012.722637
   Web of Science ®Google Scholar
• Liu, C., Hu, Y., Li, Z., Xu, J., Han, Z., & Guo, J. (2021). TriangleConv: A deep point convolutional network for recognizing building shapes in map space. ISPRS International Journal of Geo-Information, 10(10), 687. https://www.mdpi.com/2220-9964/10/10/687
   Web of Science ®Google Scholar
• Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., & Guo, B. (2021). Swin trans- former: Hierarchical vision transformer using shifted windows. arXiv. http://arxiv.org/abs/2103.14030 ( arXiv:2103.14030 [cs]).
   Google Scholar
• Li, C., Zhang, H., Wu, P., Yin, Y., & Liu, S. (2020). A complex junction recognition method based on GoogLeNet model. Transactions in GIS, 24(6), 1756–1778. https://doi.org/10.1111/tgis.12681
   Web of Science ®Google Scholar
• Mackaness, W., & Beard, K. (1993). Use of graph theory to support map generalization. Cartography and Geographic Information Science, 20(4), 210–221. https://doi.org/10.1559/152304093782637479
   Google Scholar
• Mackaness, W., Burghardt, D., & Duchêne, C. (2014). Map generalisation: Fundamental to the modelling and understanding of geographic space. In D. Burghardt, C. Duchêne, & W. Mackaness (Eds.), Abstracting geographic information in a data rich world: Methodologies and applications of map generalisation. (pp. 1–15). Springer International Publishing. https://doi.org/10.1007/978-3-319-00203-31
   Google Scholar
• Mackaness, W., & Edwards, G. (2002). The importance of modelling pattern and structure in automated map generalisation. Join ISPRS/ICA workshop on multi-scale representa- tions of spatial data. In Proceedings of the joint ISPRS/ICA workshop on multi-scale representations of spatial data (pp. 7–8).
   Google Scholar
• Ma, L., Seipel, S., Brandt, S. A., & Ma, D. (2022). A New graph-based fractality index to characterize complexity of urban form. ISPRS International Journal of Geo-Information, 11(5), 287. https://doi.org/10.3390/ijgi11050287
   Web of Science ®Google Scholar
• Mustiere, S. (1998). GALBE: Adaptative generalization. The need for an adaptative process for automated generalisation an exemple on roads. In Procesding of 1st GIS PlaNet conference.
   Google Scholar
• Porta, S., Crucitti, P., & Latora, V. (2006a). The network analysis of urban streets: A dual approach. Physica A: Statistical Mechanics and Its Applications, 369(2), 853–866. https://doi.org/10.1016/j.physa.2005.12.063
   Web of Science ®Google Scholar
• Porta, S., Crucitti, P., & Latora, V. (2006b). The network analysis of urban streets: A primal approach. Environment and Planning B: Planning and Design, 33(5), 705–725. https://doi.org/10.1068/b32045
   Web of Science ®Google Scholar
• Scheider, S., & Richter, K.-F. (2023, January). Pragmatic GeoAI: Geographic information as externalized practice. KI - Künstliche Intelligenz, 37(1), 17–31. https://doi.org/10.1007/s13218-022-00794-2
   Google Scholar
• Sester, M. (2000). Knowledge acquisition for the automatic interpretation of spatial data. International Journal of Geographical Information Science, 14(1), 1–24. https://doi.org/10.1080/136588100240930
   Web of Science ®Google Scholar
• Shea, K. S., & McMaster, R. B. (1989). When and how to generalize.
   Google Scholar
• Stanislawski, L. V., Buttenfield, B. P., Bereuter, P., Savino, S., & Brewer, C. A. (2014). Generalisation operators. In D. Burghardt, C. Duchêne, & W. Mackaness (Eds.), Abstracting geographic information in a data rich world (pp. 157–195). Springer International Publishing.
   Google Scholar
• Steiniger, S., Lange, T., Burghardt, D., & Weibel, R. (2008). An approach for the classification of urban building structures based on discriminant analysis techniques. Transactions in GIS, 12(1), 31–59. https://doi.org/10.1111/j.1467-9671.2008.01085.x
   Google Scholar
• Thomson, R. C. (2006). The ’stroke’ concept in geographic network; generalization and analysis. In A. Riedl, W. Kainz, & G. A. Elmes (Eds.), Progress in spatial data handling 12th international symposium on spatial data handling (pp. 681–697). Springer, Berlin, Heidelberg.
   Google Scholar
• Thomson, R. C., & Richardson, D. (1995). A graph theory approach to road network generalisation. In 17th international cartographic conference (pp. 1871–1880). ICA.
   Google Scholar
• Touya, G. (2010). A road network selection process based on data enrichment and structure detection. Transactions in GIS, 14(5), 595–614. https://doi.org/10.1111/j.1467-9671.2010.01215.x
   Web of Science ®Google Scholar
• Touya, G. (2012). Social welfare to assess the global legibility of a generalized map. In N. Xiao, M.-P. Kwan, M. F. Goodchild, & S. Shekhar (Eds.), Geographic information science (pp. 198–211). Springer.
   Google Scholar
• Touya, G. (2021). Multi-criteria geographic analysis for automated cartographic general- ization. The Cartographic Journal, 59(1), 18–34. https://doi.org/10.1080/00087041.2020.1858608
   Web of Science ®Google Scholar
• Touya, G., & Courtial, A. (2021). BasqueRoads: A benchmark for road network selection (Vol. 4). Copernicus Publications. https://hal.archives-ouvertes.fr/hal-03522557
   Google Scholar
• Touya, G., & Dumont, M. (2017). Progressive block graying and landmarks enhancing as intermediate representations between buildings and urban areas. In Proceedings of 20th ICA workshop on generalisation and multiple representation.
   Google Scholar
• Touya, G., & Lokhat, I. (2016). Enhancing building footprints with squaring operations. Journal of Spatial Information Science, (13), https://hal.archives-ouvertes.fr/hal-02147792
   Google Scholar
• Touya, G., & Lokhat, I. (2020). Deep learning for enrichment of vector spatial databases: Application to highway interchange. ACM Transactions on Spatial Algorithms and Systems, 6(3), 21. https://doi.org/10.1145/3382080
   Web of Science ®Google Scholar
• Touya, G., Lokhat, I., & Duchêne, C. (2019). CartAGen: An open source research platform for map generalization. Proceedings of the ICA (Vol. 2, pp. 1–9). Copernicus Publications.
   Google Scholar
• Touya, G., Zhang, X., & Lokhat, I. (2019). Is deep learning the new agent for map generalization? International Journal of Cartography, 5(2–3), 142–157. https://doi.org/10.1080/23729333.2019.1613071
   Web of Science ®Google Scholar
• Weiss, R., & Weibel, R. (2014). Road network selection for small-scale maps using an improved centrality-based algorithm. Journal of Spatial Information Science, 9(9), 71–99. https://doi.org/10.5311/JOSIS.2014.9.166
   Google Scholar
• Yan, X., Ai, T., Yang, M., & Tong, X. (2020). Graph convolutional autoencoder model for the shape coding and cognition of buildings in maps. International Journal of Geographical Information Science, 1–23. http://www.sciencedirect.com/science/article/pii/S0924271619300437
   Web of Science ®Google Scholar
• Yan, X., Ai, T., Yang, M., Tong, X., & Liu, Q. (2020). A graph deep learning approach for urban building grouping. Geocarto International, 37(10), 2944–2966. https://doi.org/10.1080/10106049.2020.1856195
   Web of Science ®Google Scholar
• Yang, M., Jiang, C., Yan, X., Ai, T., Cao, M., & Chen, W. (2022). Detecting interchanges in road networks using a graph convolutional network approach. International Journal of Geographical Information Science, 1–21. https://doi.org/10.1080/13658816.2021.2024195
   Web of Science ®Google Scholar
• Yang, M., Kong, B., Dang, R., & Yan, X. (2022). Classifying urban functional regions by integrating buildings and points-of-interest using a stacking ensemble method. International Journal of Applied Earth Observation and Geoinformation, 108, 102753. https://doi.org/10.1016/j.jag.2022.102753
   Web of Science ®Google Scholar
• Yu, W., & Chen, Y. (2022). Data-driven polyline simplification using a stacked autoencoder- based deep neural network. Transactions in GIS, 26(5), 2302–2325. https://doi.org/10.1111/tgis.12965
   Web of Science ®Google Scholar
• Zhao, R., Ai, T., Yu, W., He, Y., & Shen, Y. (2020). Recognition of building group patterns using graph convolutional network. Cartography and Geographic Information Science, 47(5), 400–417. https://doi.org/10.1080/15230406.2020.1757512
   Web of Science ®Google Scholar
• Zhou, Z., Fu, C., & Weibel, R. (2022). Building simplification of vector maps using graph convolutional neural networks. Abstracts of the ICA, 5, 1–2. https://doi.org/10.5194/ica-abs-5-86-2022
   Google Scholar
• Zhou, Q., & Li, Z. (2017). A comparative study of various supervised learning approaches to selective omission in a road network. The Cartographic Journal, 54(3), 254–264. https://doi.org/10.1179/1743277414Y.0000000083
   Web of Science ®Google Scholar
• Zhu, J.-Y., Park, T., Isola, P., & Efros, A. A. (2017). Unpaired image-to-image translation using cycle-consistent adversarial networks. In 2017 IEEE international conference on computer vision (ICCV) (pp. 2242–2251). IEEE.
   Google Scholar